We apply our proposed estimators to predict wine quality [1]} (\(n=1599\) ) from physicochemical features such as alcohol content and acidity.
We compared the performance (mean squared error) of a linear regression model, a regression tree (CART) [2]} and a random forest [3]} using 15 bootstrap samples (sample size \(n\)  with replacement).
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